The invention relates to flowmeters.
Flowmeters provide information about materials being transferred through a conduit. For example, mass flowmeters provide a direct indication of the mass of material being transferred through a conduit. Similarly, density flowmeters, or densitometers, provide an indication of the density of material flowing through a conduit. Mass flowmeters also may provide an indication of the density of the material.
Coriolis-type mass flowmeters are based on the well-known Coriolis effect, in which material flowing through a rotating conduit becomes a radially traveling mass that is affected by a Coriolis force and therefore experiences an acceleration. Many Coriolis-type mass flowmeters induce a Coriolis force by sinusoidally oscillating a conduit about a pivot axis orthogonal to the length of the conduit. In such mass flowmeters, the Coriolis reaction force experienced by the traveling fluid mass is transferred to the conduit itself and is manifested as a deflection or offset of the conduit in the direction of the Coriolis force vector in the plane of rotation.
Energy is supplied to the conduit by a driving mechanism that applies a periodic force to oscillate the conduit. One type of driving mechanism is an electromechanical driver that imparts a force proportional to an applied voltage. In an oscillating flowmeter, the applied voltage is periodic, and is generally sinusoidal. The period of the input voltage is chosen so that the motion of the conduit matches a resonant mode of vibration of the conduit. This reduces the energy needed to sustain oscillation. An oscillating flowmeter may use a feedback loop in which a sensor signal that carries instantaneous frequency and phase information related to oscillation of the conduit is amplified and fed back to the conduit using the electromechanical driver.
In one general aspect, the flowmeter includes a vibratable conduit, a driver connected to the conduit and operable to impart motion to the conduit, and a sensor connected to the conduit and operable to sense the motion of the conduit and to generate a sensor signal. A controller is connected to receive the sensor signal, and is operable to generate a raw mass-flow measurement from the sensor signal, detect a single-phase flow condition and process the raw mass-flow measurement using a first process during the single-phase flow condition to generate a first mass-flow measurement, and detect a two-phase flow condition and correct the raw mass-flow measurement using a second process during the two-phase flow condition to generate a second mass-flow measurement.
The second process may include a neural network processor to predict a mass-flow error and to calculate an error correction factor used to generate the second mass-flow measurement. The neural network processor may receive at least one input parameter and apply a set of predetermined coefficients to the input parameter.
The neural network processor may be a multi-layer perceptron neural network processor including an input layer for receiving input parameters, a hidden layer having processing nodes for applying the set of predetermined coefficients to the input parameters, and an output layer that generates an output parameter. The input parameters may include a temperature parameter, a damping parameter, a density parameter, and an apparent flow rate parameter. The flowmeter may further include a training module connected to the neural network processor to calculate an updated set of coefficients when supplied with training data. In other implementations of the flowmeter, the neural network processor may be a radial basis function network.
In other instances, the second process includes a processor to analyze a density drop parameter, to predict a mass-flow error, and to calculate an error correction factor used to generate the second mass-flow measurement. The processor may execute a bubble model routine to analyze the density drop parameter.
The first mass-flow measurement may be a validated mass-flow measurement comprising the raw mass-flow measurement and an uncertainty parameter calculated by the controller. The second mass-flow measurement may be a validated mass-flow measurement comprising a corrected mass-flow measurement generated from the raw mass-flow measurement and an uncertainty parameter calculated by the controller. The controller may generate a measurement status parameter associated with the first mass-flow measurement. The controller also may generate a measurement status parameter associated with the second mass-flow measurement.
The flowmeter may include a memory for storing sensor signal data generated from the sensor signal for use by the controller. The controller associated with the flowmeter also may include a sensor parameter processing module to analyze the sensor signal data and generate sensor signal parameters. The flowmeter also may include a state machine that uses the raw mass-flow measurement and the sensor signal parameters to detect the single-phase flow condition and the two-phase flow condition.
The controller may include an output signal generator that is operable to receive the sensor signal parameters from the sensor parameter processing module and to generate a drive signal for the driver based the sensor signal parameters. The controller may also include circuitry to generate a drive signal based on the sensor signal. The drive signal may be a digital drive signal for operating the driver. Alternatively, the drive signal may be an analog drive signal for operating the driver.
The flowmeter may include a second sensor connected to the conduit for sensing the motion of the conduit and generating a second sensor signal, with the controller being connected to receive the second sensor signal and generate the drive signal based on the first sensor signal and the second sensor signal using digital signal processing. The flowmeter may generate a measurement of a property of material flowing through the conduit based on the first and second sensor signals. The flowmeter may include a second driver, and the controller may generate different drive signals for the two drivers.
The controller may generate the measurement of the property by estimating a frequency of the first sensor signal, calculating a phase difference using the first sensor signal, and generating the measurement using the calculated phase difference. The controller may compensate for amplitude differences in the sensor signals by adjusting the amplitude of one of the sensor signals. The controller may determine a frequency, amplitude and phase offsets for each sensor signal, and scale the phase offsets to an average of the frequencies of the sensor signals. The controller may calculate the phase difference using multiple approaches and selects a result of one of the approaches as the calculated based difference. The controller may combine the sensor signals to produce a combined signal and to generate the drive signal based on the combined signal. The controller may generate the drive signal by applying a gain to the combined signal.
The controller may generate the drive signal by applying a large gain to the combined signal to initiate motion of the conduit and generate a periodic signal having a phase and frequency based on a phase and frequency of a sensor signal as the drive signal after motion has been initiated.
The flowmeter may include a second sensor connected to the conduit and operable sense the motion of the conduit, and the controller includes a first signal processor to generate the measurement, a first analog to digital converter connected between the first sensor and the first signal processor to provide a first digital sensor signal to the first signal processor, and a second analog to digital converter connected between the second sensor and the first signal processor to provide a second digital sensor signal to the controller. The first signal processor may combine the digital sensor signals to produce a combined signal and generate a gain signal based on the first and second digital sensor signals. The controller may further include a multiplying digital to analog converter connected to receive the combined signal and the gain signal from the first signal processor to generate the drive signal as a product of the combined signal and the gain signal.
The flowmeter also may include circuitry for measuring current supplied to the driver. The controller may determine a frequency of the sensor signal by detecting zero-crossings of the sensor signal and counting samples between zero crossings. The flowmeter also may include a connection to a control system, wherein the controller transmits the measurement and results of a uncertainty analysis to the control system.
In another aspect, the flowmeter includes a vibratable conduit, a driver connected to the conduit and operable to impart motion to the conduit, and a sensor connected to the conduit and operable to sense the motion of the conduit and generate a sensor signal. A controller is connected to receive the sensor signal, and is operable to detect a single-phase flow condition and process the sensor signal using a first process during the single-phase flow condition to generate a validated mass-flow measurement, and detect a two-phase flow condition and process the sensor signal using a second process during the two-phase flow condition to generate the validated mass-flow measurement.
In another aspect, the digital flowmeter includes a vibratable conduit, a driver connected to the conduit and operable to impart motion to the conduit, and a sensor connected to the conduit and operable to sense the motion of the conduit and generate a sensor signal. A controller is connected to receive the sensor signal and detect a single-phase flow condition and a two-phase flow condition. The controller includes a first processing module to analyze the sensor signal during the single-phase flow condition and to generate a first mass-flow measurement, and includes a second processing module to analyze the sensor signal during the two-phase flow condition and to generate a second mass-flow measurement. The second processing module includes a neural network processor to predict a mass-flow error and to calculate an error correction factor used to generate the second mass-flow measurement. The controller also includes output circuitry to generate a drive signal to operate the driver.
The neural network processor receives at least one input parameter and applies a set of predetermined coefficients to the input parameter. The input parameter may include a set of input parameters including a temperature parameter, a damping parameter, a density parameter, and an apparent flow rate parameter.
The controller may further include a memory for storing sensor signal data generated from the sensor signal, wherein the controller uses the stored sensor signal data. The controller may include a sensor parameter processing module to analyze the sensor signal data and generate sensor signal parameters.
The digital flowmeter may include a state machine that receives the sensor signal parameters and detects the signal-phase flow condition and the two-phase flow condition. The output circuitry is operable to receive the sensor signal parameters from the sensor parameter processing module and to generate the drive signal based on the sensor signal parameters. The controller may include a memory for storing driver signal data generated from the driver, wherein the controller uses the driver signal data. The controller may include a memory for storing sensor signal data generated from the sensor signal and driver signal data generated from the driver, wherein the controller uses the sensor signal data and the driver signal data.
The controller may generate an uncertainty parameter associated with one of the first mass-flow measurement and the second mass-flow measurement. The controller may also generate a measurement status parameter associated with one of the first mass-flow measurement and the second mass-flow measurement.
In another aspect, a method of generating a measurement of a property of material flowing through a conduit is implemented. The method includes sensing motion of the conduit, generating a measurement of a property of material flowing through the conduit based on the sensed motion, and detecting a flow condition based on the measurement of the property. If the flow condition is a single-phase flow condition, a first analysis process is applied to the measurement of the property using digital signal processing to generate a first measurement signal. If the flow condition is a two-phase flow condition, a second analysis process is applied to the measurement of the property using a neural network processor. The neural network processor operates to predict a mass-flow error based on the measurement of the property, generates an error correction factor, and applies the error correction factor to the measurement of the property to generate a second measurement signal. In addition, a drive signal is generated and used to impart motion in the conduit based on the measurement of the property.
Generating the measurement of the property may further include storing sensor signal data in a memory. The sensor signal data may be retrieved from the memory, and sensor variables, and sensor variable statistics may be calculated from the sensor signal data. The sensor variable statistics may include mean, standard deviation, and slope for each of the sensor variables.
The flowmeter may perform an uncertainty analysis on the first measurement signal and generate an uncertainty parameter associated with the first measurement signal to produce a validated mass-flow measurement signal. A measurement status parameter associated with the validated measurement signal may also be generated. The flowmeter may perform an uncertainty analysis on the second measurement signal and generate an uncertainty parameter associated with the second measurement signal to produce a validated mass-flow measurement signal. A measurement status parameter associated with the validated measurement signal may also be generated.
The techniques may be implemented in a digital flowmeter, such as a digital mass flowmeter, that uses a control and measurement system to control oscillation of the conduit and to generate mass flow and density measurements. Sensors connected to the conduit supply signals to the control and measurement system. The control and measurement system processes the signals to produce a measurement of mass flow and uses digital signal processing to generate a signal for driving the conduit. The drive signal then is converted to a force that induces oscillation of the conduit.
The digital mass flowmeter provides a number of advantages over traditional; analog approaches. For example, a processor of the flowmeter allows for the detection of a single-phase flow condition and a two-phase flow condition. The mass-flow measurement can be corrected when the flowmeter detects two-phase flow. From a control perspective, use of digital processing techniques permits the application of precise, sophisticated control algorithms that, relative to traditional analog approaches, provide greater responsiveness, accuracy and adaptability.
The digital control system also permits the use of negative gain in controlling oscillation of the conduit. Thus, drive signals that are 180xc2x0 out of phase with conduit oscillation may be used to reduce the amplitude of oscillation. The practical implications of this are important, particularly in high and variable damping situations where a sudden drop in damping can cause an undesirable increase in the amplitude of oscillation. One example of a variable damping situation is when aeration occurs in the material flowing through the conduit.
The ability to provide negative feedback also is important when the amplitude of oscillation is controlled to a fixed setpoint that can be changed under user control. With negative feedback, reductions in the oscillation setpoint can be implemented as quickly as increases in the setpoint. By contrast, an analog meter that relies solely on positive feedback must set the gain to zero and wait for system damping to reduce the amplitude to the reduced setpoint.
From a measurement perspective, the digital mass flowmeter can provide high information bandwidth. For example, a digital measurement system may use analog-to-digital converters operating at eighteen bits of precision and sampling rates of 55 kHz. The digital measurement system also may use sophisticated algorithms to filter and process the data, and may do so starting with the raw data from the sensors and continuing to the final measurement data. This permits extremely high precision, such as, for example, phase precision to five nanoseconds per cycle. Digital processing starting with the raw sensor data also allows for extensions to existing measurement techniques to improve performance in non-ideal situations, such as by detecting and compensating for time-varying amplitude, frequency, and zero offset.
The control and measurement improvements interact to provide further improvements. For example, control of oscillation amplitude is dependent upon the quality of amplitude measurement. Under normal conditions, the digital mass flowmeter may maintain oscillation to within twenty parts per million of the desired setpoint. Similarly, improved control has a positive benefit on measurement. Increasing the stability of oscillation will improve measurement quality even for meters that do not require a fixed amplitude of oscillation (i.e., a fixed setpoint). For example, with improved stability, assumptions used for the measurement calculations are likely to be valid over a wider range of conditions. The digital mass flowmeter also permits the integration of entirely new functionality (e.g., diagnostics) with the measurement and control processes. For example, algorithms for detecting the presence of process aeration can be implemented with compensatory action occurring for both measurement and control if aeration is detected.
Other advantages of the digital mass flowmeter result from the limited amount of hardware employed, which makes the meter simple to construct, debug, and repair in production and in the field. Quick repairs in the field for improved performance and to compensate for wear of the mechanical components (e.g, loops, flanges, sensors and drivers) are possible because the meter uses standardized hardware components that may be replaced with little difficulty, and because software modifications may be made with relative ease. In addition, integration of diagnostics, measurement, and control is simplified by the simplicity of the hardware and the level of functionality implemented in software. New functionality, such as low power components or components with improved performance, can be integrated without a major redesign of the overall control system.
Other features and advantages of the invention will be apparent from the following description, including the drawings, and from the claims.